However, collecting annotated data for supervised dialog policy learning is an expensive and time-consuming process. Hence, it is desirable to explore approaches to train dialog policy with limited data and transfer an existing policy with few or even no additional training data to new domains.
This practical requirement has motivated the community to research resource-constrained dialog policy learning in the past few decades. Researchers have explored approaches including employing grammar constraints for dialog policy [1]}, transfer learning [2]}, or pre-trained language models [3]}. Few-shot domain adaptation has been researched since the 2000s [4]} on both end-to-end dialog systems [5]}, [6]} as well as dialog policy learning [7]}.
